Flash-flood Potential Index mapping using weights of evidence, decision Trees models and their novel hybrid integration

Costache, Romulus

doi:10.1007/s00477-019-01689-9

Cited by 108 publications

(51 citation statements)

References 65 publications

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“…Profile curvature, defined as the surface curvature in the maximum slope direction, plays a major role in the surface flow discharge velocity. Ponding will occur in regions with negative profile values, also referred to as convex regions (Zaharia et al 2017;Siahkamari et al 2018;Costache 2019). Therefore, in this study, Eq.…”

Section: Resultsmentioning

confidence: 99%

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Assessment of check dams’ role in flood hazard mapping in a semi-arid environment

Sepehri

Ildoromi

Malekinezhad

et al. 2019

Geomatics, Natural Hazards and Risk

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This study aimed to examine flood hazard zoning and assess the role of check dams as effective hydraulic structures in reducing flood hazards. To this end, factors associated with topographic, hydrologic and human characteristics were used to develop indices for flood mapping and assessment. These indices and their components were weighed for flood hazard zoning using two methods: (i) a multi-criterion decision-making model in fuzzy logic and (ii) entropy weight. After preparing the flood hazard map by using the above indices and methods, the characteristics of the change-point were used to assess the role of the check dams in reducing flood risk. The method was used in the Ilanlu catchment, located in the northwest of Hamadan province, Iran, where it is prone to frequent flood events. The results showed that the area of 'very low', 'low' and 'moderate' flood hazard zones increased from about 2.2% to 7.3%, 8.6% to 19.6% and 22.7% to 31.2% after the construction of check dams, respectively. Moreover, the area of 'high' and 'very high' flood hazard zones decreased from 39.8% to 29.6%, and 26.7% to 12.2%, respectively.

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Section: Resultsmentioning

confidence: 99%

“…Plan curvature is the curvature of the imaginary line passing through a particular pixel that can, under certain conditions, function as the drainage point of the hillslopes (Zaharia et al 2017;Siahkamari et al 2018;Costache 2019). Therefore, fuzzy logic and Eq.…”

Section: Resultsmentioning

confidence: 99%

Assessment of check dams’ role in flood hazard mapping in a semi-arid environment

Sepehri

Ildoromi

Malekinezhad

et al. 2019

Geomatics, Natural Hazards and Risk

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“…The present study proposes the computation of both indices which were highlighted in numerous studies aiming at determining the areas prone to these natural hazards. The outputs of the analysis make this study relevant, as other studies [32,35,36,38,40,47,72] propose the computation of only one index for creating hazard maps. The optimal split of the training and testing set used in the present paper was determined through successive testing using different split ratios-80-20 and 90-10.…”

Section: Discussionmentioning

confidence: 99%

“…According to the National Administration of "Romanian Waters" and the National Meteorological Administration, the South East European Flash Flood Guidance System (SEEFFG), European Flood Awareness System (EFAS), and the Romanian Flash Flood Guidance System (ROFFG) are used to forecast flood and flash-floods for 8851 small river catchments. Previous researchers, who have used these indices to determine the areas prone to this type of natural risk phenomena, have shown the importance of knowing how to implement these indices to help local authorities to manage their interventions and minimize economic and human losses [35][36][37][38][39][40][41][42].…”

Section: General Characteristics Of the Study Areamentioning

confidence: 99%

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

Popa

Peptenatu

Drăghici

et al. 2019

Water

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The importance of identifying the areas vulnerable for both floods and flash-floods is an important component of risk management. The assessment of vulnerable areas is a major challenge in the scientific world. The aim of this study is to provide a methodology-oriented study of how to identify the areas vulnerable to floods and flash-floods in the Buzău river catchment by computing two indices: the Flash-Flood Potential Index (FFPI) for the mountainous and the Sub-Carpathian areas, and the Flood Potential Index (FPI) for the low-altitude areas, using the frequency ratio (FR), a bivariate statistical model, the Multilayer Perceptron Neural Networks (MLP), and the ensemble model MLP–FR. A database containing historical flood locations (168 flood locations) and the areas with torrentiality (172 locations with torrentiality) was created and used to train and test the models. The resulting models were computed using GIS techniques, thus resulting the flood and flash-flood vulnerability maps. The results show that the MLP–FR hybrid model had the most performance. The use of the two indices represents a preliminary step in creating flood vulnerability maps, which could represent an important tool for local authorities and a support for flood risk management policies.

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“…The inventory of torrential areas is mandatory to correctly assess the relationship between the selected influencing factors and flash-flood susceptibility [37]. Based on the work of Costache and Zaharia [38], the surfaces, such as ravines and gullies, were used for analysis, representing the actual torrential microforms.…”

Section: Inventory Of Torrential Areasmentioning

confidence: 99%

Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques

et al. 2019

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Concerning the significant increase in the negative effects of flash-floods worldwide, the main goal of this research is to evaluate the power of the Analytical Hierarchy Process (AHP), fi (kNN), K-Star (KS) algorithms and their ensembles in flash-flood susceptibility mapping. To train the two stand-alone models and their ensembles, for the first stage, the areas affected in the past by torrential phenomena are identified using remote sensing techniques. Approximately 70% of these areas are used as a training data set along with 10 flash-flood predictors. It should be remarked that the remote sensing techniques play a crucial role in obtaining eight out of 10 flash-flood conditioning factors. The predictive capability of predictors is evaluated through the Information Gain Ratio (IGR) method. As expected, the slope angle results in the factor with the highest predictive capability. The application of the AHP model implies the construction of ten pair-wise comparison matrices for calculating the normalized weights of each flash-flood predictor. The computed weights are used as input data in kNN–AHP and KS–AHP ensemble models for calculating the Flash-Flood Potential Index (FFPI). The FFPI also is determined through kNN and KS stand-alone models. The performance of the models is evaluated using statistical metrics (i.e., sensitivity, specificity and accuracy) while the validation of the results is done by constructing the Receiver Operating Characteristics (ROC) Curve and Area Under Curve (AUC) values and by calculating the density of torrential pixels within FFPI classes. Overall, the best performance is obtained by the kNN–AHP ensemble model.

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Flash-flood Potential Index mapping using weights of evidence, decision Trees models and their novel hybrid integration

Cited by 108 publications

References 65 publications

Assessment of check dams’ role in flood hazard mapping in a semi-arid environment

Assessment of check dams’ role in flood hazard mapping in a semi-arid environment

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques

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